{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/206"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/206","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Non-linear model predictive control for autonomous vehicles","abstract":"With the advent of faster computer processors and better optimization algorithms, Model Predictive Control (MPC) systems are more readily used for real-time applications. This research focuses on the application of MPC to trajectory generation of autonomous vehicles in an online manner. The operating environment is assumed to be unknown with various different types of obstacles. Models of simplified 2-D dynamics of the vehicle are developed, discretized and validated against a nonlinear CarSim vehicle model. The developed model is then used to predict future states of the vehicle. The relationship of the weight transfer to the tire slip angle is investigated. The optimal trajectory tracking problem is formulated in terms of a cost function minimization with constraints. Initially, a gradient descent method is used to minimize the cost function. A MATLAB based MPC controller is developed and interfaced with CarSim in order to test the controller on a vehicle operating in a realistic environment. The effects of varying MPC look-ahead horizon lengths on the computation time, simulation cost and the tracking performance are also investigated. Simulation results show that the new MPC controller provides satisfactory online obstacle avoidance and tracking performance. Also, a trajectory tracking criterion with goal point information is found to be superior to traditional trajectory tracking methods since they avoid causing the vehicle to retreat once a large obstacle is detected on the desired path. It is further demonstrated that at a controller frequency of 20Hz, the implementation is real-time implementable only at shorter horizon lengths.","abstract_html":"With the advent of faster computer processors and better optimization algorithms, Model Predictive Control (MPC) systems are more readily used for real-time applications. This research focuses on the application of MPC to trajectory generation of autonomous vehicles in an online manner. The operating environment is assumed to be unknown with various different types of obstacles. Models of simplified 2-D dynamics of the vehicle are developed, discretized and validated against a nonlinear CarSim vehicle model. The developed model is then used to predict future states of the vehicle. The relationship of the weight transfer to the tire slip angle is investigated. The optimal trajectory tracking problem is formulated in terms of a cost function minimization with constraints. Initially, a gradient descent method is used to minimize the cost function. A MATLAB based MPC controller is developed and interfaced with CarSim in order to test the controller on a vehicle operating in a realistic environment. The effects of varying MPC look-ahead horizon lengths on the computation time, simulation cost and the tracking performance are also investigated. Simulation results show that the new MPC controller provides satisfactory online obstacle avoidance and tracking performance. Also, a trajectory tracking criterion with goal point information is found to be superior to traditional trajectory tracking methods since they avoid causing the vehicle to retreat once a large obstacle is detected on the desired path. It is further demonstrated that at a controller frequency of 20Hz, the implementation is real-time implementable only at shorter horizon lengths.","abstract_has_math":false,"creators":["Abbas, Muhammad Awais"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Eklund, Mikael","Milman, Ruth"],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-11-01","date_published":"2011-11-01","updated_at":"2026-07-24T05:35:32Z","subjects":["Model predictive control","Autonomous vehicles","CarSim","Optimization","Gradient","Matlab","Trajectory tracking","Real-time"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/206","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Eklund, Mikael","Milman, Ruth"]},{"key":"dc:creator","label":"Author","values":["Abbas, Muhammad Awais"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2012-03-09T18:00:55Z","2022-03-25T19:02:47Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2012-03-09T18:00:55Z","2022-03-25T19:02:47Z"]},{"key":"dc:date.issued","label":"Date","values":["2011-11-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Model predictive control","Autonomous vehicles","CarSim","Optimization","Gradient","Matlab","Trajectory tracking","Real-time"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/206"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["With the advent of faster computer processors and better optimization algorithms, Model Predictive Control (MPC) systems are more readily used for real-time applications. This research focuses on the application of MPC to trajectory generation of autonomous vehicles in an online manner. The operating environment is assumed to be unknown with various different types of obstacles. Models of simplified 2-D dynamics of the vehicle are developed, discretized and validated against a nonlinear CarSim vehicle model. The developed model is then used to predict future states of the vehicle. The relationship of the weight transfer to the tire slip angle is investigated. The optimal trajectory tracking problem is formulated in terms of a cost function minimization with constraints. Initially, a gradient descent method is used to minimize the cost function. A MATLAB based MPC controller is developed and interfaced with CarSim in order to test the controller on a vehicle operating in a realistic environment. The effects of varying MPC look-ahead horizon lengths on the computation time, simulation cost and the tracking performance are also investigated. Simulation results show that the new MPC controller provides satisfactory online obstacle avoidance and tracking performance. Also, a trajectory tracking criterion with goal point information is found to be superior to traditional trajectory tracking methods since they avoid causing the vehicle to retreat once a large obstacle is detected on the desired path. It is further demonstrated that at a controller frequency of 20Hz, the implementation is real-time implementable only at shorter horizon lengths."]},{"key":"dc:title","label":"Title","values":["Non-linear model predictive control for autonomous vehicles"]}]}],"canonical_facts":{"dc:contributor.advisor":["Eklund, Mikael","Milman, Ruth"],"dc:creator":["Abbas, Muhammad Awais"],"dc:date.accessioned":["2012-03-09T18:00:55Z","2022-03-25T19:02:47Z"],"dc:date.available":["2012-03-09T18:00:55Z","2022-03-25T19:02:47Z"],"dc:date.issued":["2011-11-01"],"dc:description.abstract":["With the advent of faster computer processors and better optimization algorithms, Model Predictive Control (MPC) systems are more readily used for real-time applications. This research focuses on the application of MPC to trajectory generation of autonomous vehicles in an online manner. The operating environment is assumed to be unknown with various different types of obstacles. Models of simplified 2-D dynamics of the vehicle are developed, discretized and validated against a nonlinear CarSim vehicle model. The developed model is then used to predict future states of the vehicle. The relationship of the weight transfer to the tire slip angle is investigated. The optimal trajectory tracking problem is formulated in terms of a cost function minimization with constraints. Initially, a gradient descent method is used to minimize the cost function. A MATLAB based MPC controller is developed and interfaced with CarSim in order to test the controller on a vehicle operating in a realistic environment. The effects of varying MPC look-ahead horizon lengths on the computation time, simulation cost and the tracking performance are also investigated. Simulation results show that the new MPC controller provides satisfactory online obstacle avoidance and tracking performance. Also, a trajectory tracking criterion with goal point information is found to be superior to traditional trajectory tracking methods since they avoid causing the vehicle to retreat once a large obstacle is detected on the desired path. It is further demonstrated that at a controller frequency of 20Hz, the implementation is real-time implementable only at shorter horizon lengths."],"dc:identifier.uri":["https://hdl.handle.net/10155/206"],"dc:language.iso":["en"],"dc:subject":["Model predictive control","Autonomous vehicles","CarSim","Optimization","Gradient","Matlab","Trajectory tracking","Real-time"],"dc:title":["Non-linear model predictive control for autonomous vehicles"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:32Z"}